Compression of a collection of images using pattern separation and re-organization

ABSTRACT

Embodiments herein include an image manager that provides for image compression by rearranging an order of blocks from one or more images and then sorting and writing those blocks into one or more different images. This technique enables using a high-level of image compression to reduce a relatively large amount of pixels to a common subset of values than would ordinarily be possible with the original image(s). This can include extracting a plurality of blocks from a graphical digital image file. Each block from the graphical digital image file can be a group of pixels. The image manager analyzes each block to produce a corresponding variation value for each of the blocks, indicating a level of variation of pixel data within a respective block. The image manager sorts blocks according to the variation values, and can apply a level of image compression to each respective block, based on the variation value and/or average color of each respective block, to compress each respective block. For a given image or set of images, certain techniques disclosed herein can yield an image archive having a file size that is about two to four times smaller than conventional archiving techniques applied to the same given image or set of images.

BACKGROUND

The present disclosure relates to graphical digital images. Graphicalimages can be represented electronically in several types of formats.One type of format is a bitmap image, which is also known as a rasterimage. A bitmap, or raster, image is typically structured as a grid orarray of pixels. For each pixel, a bitmap image includes data toindicate one or more visual properties of that pixel such as color,brightness, hue, transparency, etc.

There are various file formats for electronically storing a bitmapimage, namely, Graphics Interchange Format (GIF), Portable NetworkGraphics (PNG), Tagged Image File Format (TIFF), and Joint PhotographicExperts Group (JPEG), among others. Image quality of a bitmap image cancorrespond to a total number of pixels stored within a given image file,or to a degree of lost data from image compression. As pixel count andquality increases, however, so does a file storage size. Individualbitmap files, and collections of bitmap files, can require large amountsof computer storage.

SUMMARY

File sizes of large and/or high-quality graphical digital images canconsume large amounts of computer resources for storage, and largeamounts of bandwidth for transmission. Transmitting a collection of suchimages can be even more challenging. One technique to address large filesizes is file compression or archiving. For example, one conventionalarchive format known as the ZIP file format, can compress one or morefiles of various formats by compressing each file separately. Inaddition to general data archiving formats, conventional imagecompression algorithms operate on individual files of a specific type.For example, the JPEG algorithm refers to several standards for imagecompression. These conventional standards were developed by the JointPhotographic Experts Group. JPEG compression uses a lossy compressionthat operates by replacing a range of values with a single averagevalue. This means that some image data are lost. Compressing an imageusing a high-level of JPEG compression yields a smaller file size, butalso yields a corresponding loss in image quality, that is, imagedegradation. Compressing an image using a low-level JPEG compressionmaintains a higher level of image quality, but with little reduction infile size.

Techniques disclosed herein include systems and methods for providingimage compression by rearranging an order of blocks from one or moreimages and then sorting and writing those blocks into one or more new ordifferent images. This technique enables using a high level of imagecompression to reduce a relatively large amount of pixels to a commonsubset of values than would ordinarily be possible with the originalimage(s). In one embodiment, an advantage of using techniques disclosedherein is providing image compression that is about 0.1 to 4 timesbetter than conventional compression methods, or that provides a highercompression ratio at a same image quality. In other words, for a givenset of images, certain techniques disclosed herein can yield an imagearchive having a file size that is about two to four times smaller thanconventional archiving techniques applied to the same given set ofimages. Alternatively, for the given set of images, certain techniquesdisclosed herein can yield an image archive of a same given file size ofa conventional archive, but with archived images having four timesbetter image quality than with images archived using conventionalmethods.

In one embodiment, an image manager extracts a plurality of blocks froma graphical digital image file. The graphical digital image file definesan array of pixels for representing a graphical image. Each block fromthe graphical digital image file comprises a group of pixels. In otherwords, the array or grid of pixels can be divided or sectioned intogroups of pixels. The image manager analyzes each block from theplurality of blocks to produce a corresponding variation value for eachof the blocks. Each corresponding variation value indicates a level ofvariation of pixel data within a respective block. For example, a givenblock of pixels can have pixels with many different colors or with justa few colors. Blocks having many colors or much contrast have largeramounts of variation of pixel data, while blocks having just a fewcolors or little contrast have smaller amounts of variation among pixeldata.

The image manager sorts the plurality of blocks according to thevariation values produced for the blocks. For example, the image managercan sort the plurality of blocks from blocks having low variation valuesto blocks having high variation values. The image manager thenidentifies a level of image compression applied to each respective blockbased on the variation value of each respective block. The level ofimage compression affects image quality. For example, a high level ofimage compression means that more data is lost during compression,resulting in a smaller image file size. Thus, high compression typicallycorresponds to high degradation. Likewise, a low level of imagecompression means that less data is lost during compression, resultingin a larger image file size. Low image compression typically correspondsto low image degradation by essentially keeping raw data. There can beany number of selectable levels of image compression ranging from noloss of data to extensive loss of data. The image manager compresseseach respective block using the level of image compression identified toapply to each respective block.

In addition to sorting the blocks based on variation values, the imagemanager can also sort the blocks according to color values produced oridentified for the blocks. This can be, for example, an average colorvalue of pixels within a given block.

In another embodiment, the image manager extracts the plurality ofblocks from graphical digital image files from a first plurality ofgraphical digital image files. Each graphical digital image file definesan array of pixels for representing the graphical image. Each block isextracted from the first plurality of graphical digital image files.Each block can be a contiguous group of pixels, such as a rectangulargroup of pixels. The image manager analyzes each block from theplurality of blocks to produce a corresponding variation value for eachof the blocks. Each corresponding variation value indicates a level ofvariation of pixel data within a respective block. The image managersorts the plurality of blocks according to the variation values producedfor the blocks.

The image manager can then organize the plurality of blocks into asecond plurality of graphical digital image files. Each respectivegraphical digital image file, from the second plurality of graphicaldigital image files, is created from blocks having similar variationvalues. Optionally, the image manager can organize the plurality ofblocks into a second plurality of graphical digital image files based onaverage color values in addition to the variation values. In otherwords, each image within a collection of images is divided into blocksand then the image manager extracts, sorts, and reorganizes these blocksinto new images to create a second collection of images using theextracted blocks.

The image manager compresses each respective graphical digital imagefile, from the second plurality of graphical digital image files, usinga predetermined image compression algorithm. For example, after creatingthe collection of new images, image manager can use a JPEG algorithm ora TIFF algorithm to compress each of the new images. With each new imagecomposed of blocks having similar variation values, the predeterminedimage compression algorithm can compress pixel data more efficiently.

The image manager maintains a map of each block that identifies alocation of each respective block with the first plurality of graphicaldigital image files and within the second plurality of graphical digitalimage files, so that after decompressing each respective graphicaldigital image file from the second plurality of graphical digital imagefiles, the image manager can then reorganize the decompressed blocksinto the graphical digital image files from the first plurality ofgraphical digital image files.

Yet other embodiments herein include software programs to perform thesteps and operations summarized above and disclosed in detail below. Onesuch embodiment comprises a computer program product that has acomputer-storage medium (e.g., a tangible computer readable storagemedia, disparately located or commonly located storage media, computerstorage media or medium, etc.) including computer program logic encodedthereon that, when performed in a computerized device having a processorand corresponding memory, programs the processor to perform theoperations disclosed herein. Such arrangements are typically provided assoftware, firmware, microcode, code data (e.g., data structures), etc.,arranged or encoded on a computer readable storage medium such as anoptical medium (e.g., CD-ROM), floppy disk, hard disk, one or more ROMor RAM or PROM chips, an Application Specific Integrated Circuit (ASIC),and so on. The software or firmware or other such configurations can beinstalled onto a computerized device to cause the computerized device toperform the techniques explained herein.

Accordingly, one particular embodiment of the present disclosure isdirected to a computer program product that includes one or morecomputer storage media having instructions stored thereon for supportingoperations such as: extracting a plurality of blocks from a graphicaldigital image file, the graphical digital image file defining an arrayof pixels for representing a graphical image, each block from thegraphical digital image file being a group of pixels; analyzing eachblock from the plurality of blocks to produce a corresponding variationvalue for each of the blocks, each corresponding variation valueindicating a level of variation of pixel data within a respective block;sorting the plurality of blocks according to the variation valuesproduced for the blocks; identifying a level of image compression toapply to each respective block based on the variation value of eachrespective block, the level of image compression affecting imagequality; and compressing each respective block using the level of imagecompression identified to apply to each respective block. Theinstructions, and method as described herein, when carried out by aprocessor of a respective computer device, cause the processor toperform the methods disclosed herein.

Other embodiments of the present disclosure include software programs toperform any of the method embodiment steps and operations summarizedabove and disclosed in detail below.

Of course, the order of discussion of the different steps as describedherein has been presented for clarity sake. In general, these steps canbe performed in any suitable order.

Also, it is to be understood that each of the systems, methods,apparatuses, etc. herein can be embodied strictly as a software program,as a hybrid of software and hardware, or as hardware alone such aswithin a processor, or within an operating system or within a softwareapplication, or via a non-software application such a person performingall or part of the operations. Example embodiments as described hereinmay be implemented in products and/or software applications such asthose manufactured by Adobe Systems Incorporated of San Jose, Calif.,USA.

As discussed above, techniques herein are well suited for use insoftware applications supporting electronic image management, storage,and compression. It should be noted, however, that embodiments hereinare not limited to use in such applications and that the techniquesdiscussed herein are well suited for other applications as well.

Additionally, although each of the different features, techniques,configurations, etc. herein may be discussed in different places of thisdisclosure, it is intended that each of the concepts can be executedindependently of each other or in combination with each other.Accordingly, the present invention can be embodied and viewed in manydifferent ways.

Note that this summary section herein does not specify every embodimentand/or incrementally novel aspect of the present disclosure or claimedinvention. Instead, this summary only provides a preliminary discussionof different embodiments and corresponding points of novelty overconventional techniques. For additional details and/or possibleperspectives of the invention and embodiments, the reader is directed tothe Detailed Description section and corresponding figures of thepresent disclosure as further discussed below.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other objects, features, and advantages of theinvention will be apparent from the following more particulardescription of preferred embodiments herein as illustrated in theaccompanying drawings in which like reference characters refer to thesame parts throughout the different views. The drawings are notnecessarily to scale, with emphasis instead being placed uponillustrating the embodiments, principles and concepts.

FIG. 1 is an example block diagram of an image manager operating in acomputer/network environment according to embodiments herein.

FIG. 2 is a flowchart illustrating an example of a process supporting animage manager according to embodiments herein.

FIG. 3 is an example conceptual illustration of an image reorganizationprocess according to embodiments herein.

FIG. 4 is a flowchart illustrating an example of a process supporting animage manager according to embodiments herein.

FIG. 5-6 is a flowchart illustrating an example of a process supportinga image manager according to embodiments herein.

FIG. 7 is a flowchart illustrating an example of a process supporting animage manager according to embodiments herein.

DETAILED DESCRIPTION

According to one example embodiment, an image manager provides for imagecompression by rearranging an order of blocks from one or more imagesand then sorting and writing those blocks into one or more differentimages. This technique enables using a high level of image compressionto reduce a relatively large amount of pixels to a common subset ofvalues than would ordinarily be possible with the original image(s).

More specifically, the image manager can subdivide individual images, oreach image of a collection of images, into a series of blocks or tilesof predetermined dimensions. The image manager can convert each blockfrom Red Green Blue (RGB) color values to Y′CbCr (luma+chromacomponents), such as using a specific International Color Consortium(ICC) profile. The image manager can reduce a resolution of chrominancecomponents (Cb and Cr) by factor of two or four to match a JPEG/Exifalgorithm to produce similar ranges of values. JPEG compression usesquantization, which is a lossy compression that consists of replacing arange of values by a single average value. Since JPEG uses quantization,the image manager can promote or optimize such quantization compressionby rearranging an order of blocks, sorting and writing the blocks into astorage image so that the quantization compression, even set at thehighest level of quality (least degradation), can reduce a larger amountof pixels to a common subset of values than otherwise possible.

The image manager applies a discrete cosine transform (DCT), or otherpredetermined mathematical data transform, to each block to produce afrequency (variation value). The result of the transform is a matrix ofa same size as a matrix input size. For example, if a given block werean 8×8 matrix, then applying a DCT will produce an 8×8 matrix with lowfrequency on [0,0] to higher frequency on [7,7]. Typically, for complexblocks an output matrix tends to be filled with higher values. An outputmatrix with only [0,0] different than zero means that the blocks have novariation, that is, they are a unified color.

The image manager sorts the blocks on the produced frequency. Blockswith lower frequency values can be stored first. The image manager canuse this technique with different JPEG standards such as, for instance,with JPEG 2000 using a discrete wavelet transform (DWT) instead of aDCT. Alternatively, image manager can use a fast cosine transform (FCT)to determine the frequency of each block. Storage files containing theblocks from the collection of images are then saved as a new collectionof JPEG images. The image manager can then select any level ofcompression to apply to the storage files in the new collection ofimages. A selection of a low level of compression results in higherimage quality, while selecting a high level of compression candramatically reduce file size.

The image manager can use a database to store a location of each blockbelonging to a given image. This database can also be contained in ametadata field that the JPEG format supports, to avoid transportation ofan additional file.

One advantage of using techniques disclosed herein is providing imagecompression yielding file size or quality results that are about two tofour times better than conventional methods. In other words, for a givenset of images, techniques disclosed herein can yield an image archivehaving a file size that is about two to four times smaller thanconventional archiving techniques applied to the same given set ofimages. Alternatively, for the same given set of images, techniquesdisclosed herein can yield an image archive of a same given file sizehaving about two to four times better image quality than with usingconventional methods that storing at the same given file size.

By first sorting blocks based on variation values, the image manager canuse quantization compression—even at the highest level of quality—todramatically reduce stored pixel data. The image manager thus provides abetter compression ratio with equivalent image quality than with usingconventional compression methods. This saves computer storage space andreduces the amount of data to send by applying the efficiency of JPEGcompression on a collection of images. The result is a much bettercompression ratio (observed 0.8 to 0.4:1) with a quality equivalent tousing standard per-image compression that gives a 0.95:1 to 0.7:1compression ratio.

Now more particularly, FIG. 1 shows a general overview of the imagemanager and how it can be tied to an example physical apparatus, such asa computer and related components. After this description of FIG. 1appears a more detailed description of processes and methods executed bythe image manager itself. This subsequent description will explain theflow charts and reference illustrations in the figures to describeexample embodiments.

In FIG. 1, computer system 110 is shown connected to display monitor 130for displaying a graphical user interface 133 for a user 106 to use aimage manager 140 for archiving or compressing images, using inputdevices 116, including storing a collection of images. Repository 181can optionally be used for storing image data and blocks both before andafter processing. Input devices 116 can include one or more devices suchas a keyboard, computer mouse, etc.

Note that the following discussion provides a basic embodimentindicating how to carry out functionality associated with the imagemanager 140 as discussed above and below. It should be noted, however,that the actual configuration for carrying out the image manager 140 canvary depending on a respective application. For example, as previouslydiscussed, computer system 110 can include one or multiple computersthat carry out the processing as described herein.

In different embodiments, computer system 110 may be any of varioustypes of devices, including, but not limited to, a personal computersystem, desktop computer, laptop, notebook, or netbook computer,mainframe computer system, handheld computer, workstation, networkcomputer, application server, storage device, a consumer electronicsdevice such as a camera, camcorder, set top box, mobile device, videogame console, handheld video game device, or in general any type ofcomputing or electronic device.

As shown, computer system 110 of the present example includes aninterconnect 111 that couples a memory system 112, a processor 113, 110interface 114, and a communications interface 115.

I/O interface 114 provides connectivity to peripheral devices such asinput devices 116 including a computer mouse, a keyboard, a selectiontool to move a cursor, display screen, etc.

Communications interface 115 enables the image manager 140 of computersystem 110 to communicate over a network and, if necessary, retrieve anydata required to create views, process content, communicate with a user,etc. according to embodiments herein.

As shown, memory system 112 is encoded with image manager 140-1 thatsupports functionality as discussed above and as discussed furtherbelow. Image manager 140-1 (and/or other resources as described herein)can be embodied as software code such as data and/or logic instructionsthat support processing functionality according to different embodimentsdescribed herein.

During operation of one embodiment, processor 113 accesses memory system112 via the use of interconnect 111 in order to launch, run, execute,interpret or otherwise perform the logic instructions of the imagemanager 140-1. Execution of the image manager 140-1 produces processingfunctionality in image manager process 140-2. In other words, the imagemanager process 140-2 represents one or more portions of the imagemanager 140 performing within or upon the processor 113 in the computersystem 110.

It should be noted that, in addition to the image manager process 140-2that carries out method operations as discussed herein, otherembodiments herein include the image manager 140-1 itself (i.e., theun-executed or non-performing logic instructions and/or data). The imagemanager 140-1 may be stored on a tangible computer readable storagemedium including computer readable storage media such as floppy disk,hard disk, optical medium, etc. According to other embodiments, theimage manager 140-1 can also be stored in a memory type system such asin firmware, read only memory (ROM), or, as in this example, asexecutable code within the memory system 1012.

In addition to these embodiments, it should also be noted that otherembodiments herein include the execution of the image manager 140-1 inprocessor 113 as the image manager process 140-2. Thus, those skilled inthe art will understand that the computer system 110 can include otherprocesses and/or software and hardware components, such as an operatingsystem that controls allocation and use of hardware resources, ormultiple processors.

Functionality supported by computer system 110 and, more particularly,functionality associated with image manager 140 will now be discussedvia flowcharts and illustrations in FIG. 2 through FIG. 7. For purposesof the following discussion, the image manager 140 or other appropriateentity performs steps in the flowcharts.

Now describing embodiments more specifically, FIG. 2 is a flow chartillustrating embodiments disclosed herein.

In FIG. 2, collection of images 205 includes images 207-1 and 207-n. Forexample, collection of images 205 can include multiple images stored ona digital camera, or stored on another digital device or media. In step207, image manager 140 accesses one or more images. For example, imagemanager 140 selects a collection of images from a given digital camera,or selects a collection of images from a given file directory, orselects an individual image. In step 209, for each accessed image, imagemanager 140 extracts blocks 210-1 to 210-m. A block as described hereincan also be referred to as a tile.

Each block is a collection, array, or group of pixels. Any designateddimensions can define a given block. For example, blocks could be 8×8pixels, or 16×8 pixels, or 32×32 pixels, or 62×62 pixels, and so forth.The dimensions of each block, however, can affect image quality. Forexample, dividing an image into relatively large blocks (62×62) canimprove compression for smaller file sizes, but can also result in acorresponding loss of image quality because tiling (visible checkerpattern) would become more perceptible to the human eye. On the otherhand, using a relatively small block (8×8) can enable an acceptable ortolerable level of image compression while maintaining image quality.Using a small block can be more tolerable because the human eye cannotdistinguish an 8×8 block without magnification. Note that the blocks donot need to be squares but can be rectangles or other shapes. A givenblock shape can be based on a shape of an original image to helpconserve an original shape of that image. Selecting all blocks to be afixed size and to have contiguous pixels, however, can reduce overheadand problems relating to mapping the blocks. Image manager 140 maintainsa map or inventory of stored blocks in database 281.

In step 215, image manager 140 analyzes each block to produce variationvalues to identify which blocks are the same or similar. For example, inone embodiment, image manager 140 can perform a quantization on theblocks using a discrete cosine transform. The result of the discretecosine transform is a frequency value of pixel data. For a given blockhaving very similar colors, or one blue color, then a resultingfrequency or variation output value, for the given block, might be zeroor one or two (based on an example arbitrary scale from zero to 100,with zero being no variation and 100 being a maximum variation). Avariation value or frequency of zero would mean that all blocks areidentical. For a given block having very different colors and brightnessand so forth, then a variation value might be very high such as thevalue of 93 or 94 (in the same example scale from zero to 100). A blockwith a variation value of 100 would then represent a block having amaximum amount of variation. Such blocks having many different colorscannot be reduced to one single color without losing a lot of data.

While certain JPEG algorithms use the discrete cosine transform processfor compression operations, image manager 140 instead uses the discretecosine transform, or other mathematical transform, to sort blocks to beable to better predict compression for a given image file. Certain otherJPEG algorithms use discrete wavelet transform, while other file formatsmight use a fast Fourier transform. Image manager 140 can optionally usethe discrete wavelet transform or fast Fourier transform, or any otherfrequency identification method used by a given compression codecselected to compress an image.

In step 225, image manager 140 inserts each block into a storage mapsorted by variation value. For example, image manager 140 can sort eachblock from low-frequency to high-frequency, meaning that blocks havingfew variations are listed or are grouped together, and blocks havingmany variations are grouped with other blocks having many variations.Image manager 140 maps or ties each block to the original image fromwhich a given block was extracted, and into which the given block willbe inserted. Thus, each extracted block is identified and tracked.

In step 227, image manager 140 identifies whether there are more blocksto analyze. If there are no more blocks to analyze from a given image,then image manager identifies whether there are more images, such asfrom a collection of images, for extraction and analysis of blocks. Ifall blocks from all selected images have been analyzed, then imagemanager 140 proceeds to reorganizing the blocks.

In step 230, image manager 140 creates new images with ordered or sortedblocks, based on the variation values of each block. In step 235, imagemanager 140, for each new image, dequeues a given block and inserts thegiven block in the new image as a next block. For example, creating newimages can result in multiple new images wherein each new image iscomposed of blocks that are visibly similar. If image manager 140 cancreate, for example, a new given image composed of blocks of a givenvariation value (or variation value range) and/or color. This couldresult in one image being composed entirely of blocks colored a givenshade of blue, and another image being composed entirely of blocks of agiven brightness of green, and another image being composed entirely ofblocks of multiple different contrasting colors. Thus, each new imagemight appear visually as a mosaic of similar blocks extracted from thecollection of images, if rendered for display. In one sense, the newimage could be considered as a fake image in that the new image may notbe recognizable as a photograph taken from a camera. At the same time,the new image can be an actual image file to which any number ofcompression techniques can be applied.

FIG. 3 illustrates a conceptualization of this block extraction,sorting, and reorganization process. Images 710-1, 710-2, and 710-3represent images from collection of images 205, and can be considered afirst plurality of graphical digital image files. Each of these imagesmight be snapshots captured by a digital camera of various scenes at abeach. In this example, each image is divided into nine blocksrepresented by the dotted lines. Note that each image has several blocksthat are primarily white or empty. These empty blocks represent a blue,sky background. There are several blocks filled with dots representingsand, and several blocks representing various individuals at the beach.

Upon performing a discrete cosine transform to each of these blocks,image manager 140 can then identify blocks representing the blue sky ashaving a low variation value, blocks containing sand as having moderatevariation values, and blocks containing individuals as having highvariation values.

Images 720-1, 720-2, and 720-3 represent new images created with theextracted blocks. Note the block 720-1 is composed primarily of blocksrepresenting sky, which corresponds to low variation values because eachblock has an array of similar pixel data and variation. Block 720-2 iscomposed primarily of blocks representing sand. While each blockrepresenting sand has more variation within itself than blocksrepresenting sky, each block representing sand has a similar variationvalue to other blocks representing sand, and thus these blockscontaining sand are grouped together into a new image. Image 720-3 iscomposed primarily of blocks having high variation values depictingindividuals that can vary dramatically in shape and color.

The new collection of images, (images 720-1, 720-2, and 720-3) containsthe same amount of pixels or blocks as the first collection of images(710-1, 710-2, and 710-3), but if the new collection of images weregraphically rendered, as in the conceptualization of FIG. 3, then thenew collection of images might appear completely different from thefirst collection of images. While this simplified, conceptualillustration depicts the new collection of images as having the samequantity and size as the first collection of images, such conformity ofsize and quantity is not necessary. The new collection of images doesnot need to have the same number of images as the first collection ofimages, or even the same size images.

For example, instead of the three images 710-1, 710-2, and 710-3 fromthe collection of images in FIG. 3, a given image collection might have100 pictures or 1000 pictures with many of those pictures including bluesky. The new collection of images can include one or more individualimages composed of pieces of blue sky. Image manager 140, can create onerelatively large image to include all blocks having blue sky, or, in thealternative, image manager 140 can create multiple individual imageshaving pieces of blue sky according to variation values. In a givencollection of images there may be blocks of one single color repeatedmany times. Image manager 140 can create a new image comprised of eachof these identical blocks. Such a new image will not require muchcompression to reduce its file size to a relatively small size. With alow-level of image compression, blocks associated with this image willhave very good quality while having a very small size on disk.

Returning to FIG. 3, and the collection of new images, image manager 140can designate different levels of compression to apply to each of images720-1, 720-2, and 720-3. For example, image manager 140 might apply alow-level of image compression to image 720-1 (blocks of sky), amoderate level of image compression to image 720-2 (blocks of sand), anda high-level of image compression to image 720-3 (blocks of people). Theresult of applying variable compression levels, based on variationvalues of blocks within each new image, is better quality and smallerimage file sizes when using a conventional image compression algorithmto complete compression of the new images.

Using variable image compression, based on variation values of blocks,efficiently provides compression with high image quality, in partbecause of how the human eye functions. The human eye is very sensitiveto the tiling effect on areas having low variation. For example, animage of a white wall has relatively low frequency or variation values.Compressing the image of the white wall at a high compression settingresults in a perceived low image quality because spots or tilesresulting from image compression are easily noticeable against a uniformwhite background. This effect is generally undesirable. For example, itis easier for the human eye to perceive a dark speck on a white wallthen to perceive a dark speck among grains of sand or blades of grass.

On the other hand, blocks with high variation values can be compressedat a higher level of compression (meaning more data loss) without aperceived loss of image quality. For example, blocks from an area of theimage displaying sand typically have multiple different colors of sandgrains such as tan, brown, and black. Thus there may already existseveral small black grains of sand among other grains. If applying ahigh-level of image compression results in some blocks turning black,then a few extra black blocks among mixed colors of grains of sand willbe very difficult for the human eye to notice. This degradation can alsobe described as pixilation. Thus, the human eye can tolerate lots ofpixilation in areas of an image with high frequency or high variationvalues, such as areas with lots of color or motion.

Embodiments herein take advantage of the realization by applyingdifferent levels of compression to the groupings of blocks depending ona variation of pixels for the blocks. Less compression or lower-losscompression is applied to blocks where loss of pixel data would beeasily detectable by the human eye. More compression or more higher-losscompression settings can be applied to blocks in which degradation ofpixel data (due to compression) would be less detectable by the humaneye.

In step 240, the image manager 140 compresses each new imageindividually using a predetermined algorithm, such as a JPEG algorithm.If there are more images (step 241) then image manager 140 repeats theprocess of constructing new images with the sorted blocks. In step 250,image manager 140 creates an image archive, such as an archive of JPEGimages. For example, this archive could be stored within a ZIP file, aPDF file, or other data container.

Conventionally, JPEG algorithms apply one compression setting or levelto one image. The JPEG algorithm does not apply or cannot applydifferent compression ratios to different parts of a single image. Thus,the JPEG algorithm applies one compression ration/compression level toan entire image.

JPEG algorithms typically operate with a three-step method. The firststep is a data transform, such as DCT, which defines the frequency ofblocks. The second step is quantization, which is the process of mergingor registering several pixels into fewer pixels. Quantization loses somepixel data. The third step is Huffman encoding, which is a process torearrange data so that the data fills less space in a file. The Huffmanencoding is a binary tree that puts very common items together to beable to indicate a number of instances of a certain type of block, whichallows compression to be high because the Huffman encoding only needs tostore one copy of several identical blocks. The JPEG algorithm names andidentifies these blocks within an image so that during decompressionthese identical blocks can be copied to corresponding locations orpositions within the JPEG image. Huffman encoding is a losslesscompression. The Quantization process of JPEG compression is, bydefinition, lossy. JPEG technology can use variations on the discretecosine transform including fast cosine transform FCT. Image manager 140can operate with DCT, DWT, SWT, FWT, or any other mathematical transformto produce variations values.

Thus, image manager 140 performs preprocessing on an image or collectionof images prior to application of a JPEG algorithm or other imagecompression algorithm. This preprocessing enables better imagecompression by optimizing images for compression.

Image manager 140 can operate on a single image, or on a collection ofimages. Applying the process to a single image can result in 10-20%better compression. Applying the process to a collection of images canproduce an overall file size that is about two to four times smaller.For example, a collection of images that has a total disk size of 1 GBcan be reduced to less than 300 MB while maintaining a same imagequality of the 1 GB image collection. This significant size reduction isin part due to the economies of scale. With a relatively largecollection of images having billions of pixels, there is typically awide range of variation values and many blocks that are identical ornearly identical. With so many identical or nearly identical blocks,newly created images can be very close to being a single color. Withmany single color images, image compression is inherently more efficientbecause the Huffman encoding will be more optimized.

Another embodiment of the image management process appears in the flowchart of FIG. 4.

In step 300, image manager 140 extracts a plurality of blocks from agraphical digital image file. The graphical digital image file definesan array of pixels for representing a graphical image, such as in abitmap or raster image. Each block from the graphical digital image fileis a group of pixels. Thus, the array of pixels is segmented into groupsof pixels.

In step 310, image manager 140 analyzes each block from the plurality ofblocks to produce a corresponding variation value for each of theblocks. Each corresponding variation value indicates a level ofvariation of pixel data within a respective block. For example, imagemanager 140 can execute a discrete cosine transform or discrete wavelettransform or other transform to identify a frequency of variation amongpixel data within each respective block.

In step 320, image manager 140 sorts the plurality of blocks accordingto the variation values produced for the blocks. For example, imagemanager 140 can sort the blocks from low variation values to highvariation values.

In step 330, image manager 140 identifies a level of image compressionto apply to each respective block based on the variation value of eachrespective block. The level of image compression affects image qualityand/or defines a compression ratio. Thus, high compression results inhigh degradation meaning that pixel data can be discarded and arendering of the image can be perceived as lower quality by observationof the human eye.

In step 340, image manager 140 compresses each respective block usingthe level of image compression identified to apply to each respectiveblock. In other words some blocks may have been tagged for low imagecompression, while other blocks have been tagged for moderate imagecompression, while yet other blocks have been tagged for high imagecompression. Each block can be compressed using a different compressionlevel than applied to other blocks depending on variation values. Thatis, blocks with higher variation are compressed using a lossiercompression algorithm, while blocks with lower variation are compressedusing a less lossy compression algorithm or algorithm setting.

Another embodiment of the image management process appears in the flowchart of FIGS. 5-6. This embodiment expands on embodiments in FIG. 4with additional alternative features.

In step 300, image manager 140 extracts a plurality of blocks from agraphical digital image file. The graphical digital image file definesan array of pixels for representing a graphical image. Each block fromthe graphical digital image file is a group of pixels.

In step 310, image manager 140 analyzes each block from the plurality ofblocks to produce a corresponding variation value for each of theblocks. Each corresponding variation value indicates a level ofvariation of pixel data within a respective block.

In step 315, image manager 140 analyzes each block from the plurality ofblocks to produce a corresponding color value for each of the blocks,each corresponding color value indicating an average color of pixelswithin a respective block. In the previous step, identifying variationvalues typically produces a number representing a frequency of variationof pixel data. This variation value may not always designate a color.Image manager 140, however, can also identify or produce one or morecolors to identify each respective block. For example, image manager 140can calculate an average color of pixels within a given block and storeor associate this average color indication with the variation value.

In step 320, image manager 140 sorts the plurality of blocks accordingto the variation values produced for the blocks.

In step 322, image manager 140 sorts the plurality of blocks accordingto the color values produced for the blocks. Thus, image manager 140 cansort blocks by both variation values and by color. Sorting blocks intodifferent images based on color enables the Huffman encoding (as part ofJPEG image compression) to be more optimized.

Sorting both on frequency and color improves image compression. Forexample, there may be two blocks having a same frequency or variationvalue, but one block is predominantly blue while the other block ispredominately red. If image manager 140 creates a new image forcompression with both blue and red blocks, then, depending on the levelof compression and data loss as applied to an entire image (especially astrong level of quantization), some of the blue blocks can becomereddish while some of the red blocks can become bluish. When thesecolor-modified blocks are reorganized into the original image, then suchcolor modification can be noticeable and undesirable. For example, ablock belonging to a blue sky region of an original image may havebecome reddish during compression and thus placing the now reddish blockwithin a blue region of a reconstructed image might be noticeable.Sorting by variation and color prevents the JPEG algorithm from creatingartifacts that the human eye can perceive. Sorting by color does notsubstantially affect a compression ratio, but can affect a quality ofthe image.

Image manager 140 can execute the dual sorting in various ways. Forexample, image manager 140 can first sort and group blocks within aparticular range of variation values, and then within that range executea secondary sorting based on color or average color of each respectiveblock. The range of variation values can be based on percentages orequal division of all blocks. For percentage sorting, one group ofblocks might contain all blocks having a variation of 0-2%. Within thispercentage range, image manager 140 can then sort blocks based on anynumber of predetermined colors or color ranges. Both the variationsorting and color sorting can be organized and maintained in a hashtable.

In step 330, image manager 140 identifies a level of image compressionto apply to each respective block based on the variation value of eachrespective block. The level of image compression affects image qualityor the level of image compression indicates a compression ratio.

In step 332, the level of image compression indicates a compressionsetting of a predetermined image compression algorithm. This compressionsetting can correspond to a specific compression ratio. For example,there are several conventionally available image compression algorithmssuch as TIFF, PNG, and JPEG. Some image compression algorithms enableselectable levels of image compression, which can correspond to anamount of file size reduction and/or image degradation.

In step 333, for blocks having small variation values in comparison toother blocks in the plurality of blocks, image manager 140 identifies alow level of image compression to apply to each corresponding respectiveblock. The low level of image compression results in a compressed blockhaving relatively high image quality in comparison to other blocks fromthe plurality of blocks. As previously explained, degrading blockshaving low variation values is more likely to result in artifacts,tiling, or other easily observed defects in a reconstructed ordecompressed image rendered for a human eye. Applying a low level ofimage compression to such low frequency areas thus preserves imagequality.

In step 334, for blocks having large variation values in comparison toother blocks in the plurality of blocks, image manager 140 identifies ahigh level of image compression to apply to each correspondingrespective block. The high level of image compression results in acompressed block having relatively low image quality in comparison toother blocks from the plurality of blocks. Also, as previouslyexplained, the human eye can tolerate more image degradation in areas ofhigh frequency/high variation because the human eye cannot easily detectmodifications or changes to such areas of a rendered image.

In step 335, image manager 140 organizes the plurality of blocks intomultiple new graphical digital image files, each respective newgraphical digital image file being created from blocks having similarvariation values. Thus, image manager 140 can either apply compressionto individual blocks from the image, or can first create a new set orcollection of images using the extracted blocks from the original image.Thus, image manager 140 might extract blocks from an original image,analyze and sort the blocks, then create 4, 10, 20, or more new imagesusing the extracted blocks. Since each block is used once in the newimages, then the new images will have smaller dimensions then theoriginal image.

In step 340, image manager 140 compresses each respective block usingthe level of image compression identified to apply to each respectiveblock.

In step 342, image manager 140 applies the predetermined imagecompression algorithm to each block according to the level of imagecompression identified to apply to each respective block. For blocksidentified to apply a high compression ratio, then image manager 140applies high compression. The level of image compression identifieddepends on pixel data of each block and image. For example, for an imageof people at the beach depicting sand and sky, image manager 140 mightapply a wide range of compression levels. If a given image, instead,depicts a scene filled entirely with foliage, then most blocks from thatimage will probably receive a higher level of image compression.

In step 344, image manager 140 compresses each respective new graphicaldigital image file using a predetermined image compression algorithm.The predetermined image compression algorithm being a joint photographicexperts group (JPEG) compression algorithm. Image manager 140 can selectfrom any of several standards such as JPEG or JPEG 2000.

In step 350, image manager 140 maintains a storage map that identifies alocation of each block in the graphical digital image file. Blocks areextracted from a first image and reorganized for compression, but theseblocks will need to be subsequently accessed individually. Maintaining astorage map then assists with block identification and management.

In step 360, image manager 140 decompresses each respective block.Blocks typically need to be decompressed to be able to be graphicallyrendered on a display.

In step 370, image manager 140 uses the storage map to reconstruct thegraphical digital image file using decompressed blocks. Ultimately,original images need to be reconstructed or reassembled for viewing.Using the storage map assists with the reassembly process.

Another embodiment of the image management process appears in the flowchart of FIG. 7.

In step 610, image manager 140 extracts a plurality of blocks fromgraphical digital image files from a first plurality of graphicaldigital image files. Each graphical digital image file defines an arrayof pixels for representing a graphical image. Each block extracted fromthe first plurality of graphical digital image files is a contiguousgroup of pixels. For the first plurality of graphical digital imagefiles, image manager 140 can access a collection of images from a filedirectory, from a digital camera, from another electronic device, etc.The number of images in the collection can be any number. There could betens, hundreds, thousands, or more images included in the firstplurality of graphical digital images. Whatever the number of imagesselected for inclusion in the first plurality, image manager 140 canextract blocks across all of these images. Each image can be dividedinto fixed or variable size blocks, and then image manager 140 extractsall of these blocks. Depending on the collection of images anddesignated block size, such extraction can produce thousands, millions,billions, or more, of blocks aggregated from the images.

In step 620, image manager 140 analyzes each block from the plurality ofblocks to produce a corresponding variation value for each of theblocks. Each corresponding variation value indicates a level ofvariation of pixel data within a respective block. In other words, eachindividual block is separately analyzed to produce a variation value ofpixel data within that block. For example, if the block were 8×8 pixels,then there would be 64 pixels within a given block. Some blocks mighthave pixels of predominantly one color, while other blocks have pixelswith many different colors. Each pixel can also include other graphicaldata such as luminance, chrominance, hue, etc. If each pixel within agiven block has data that varies substantially from other pixel datawithin the block, then image manager 140 will produce a relatively highor large variation value. If pixel data within a given block is all verysimilar, then this will produce a relatively low or small variationvalue.

Image manager 140 can further analyze each block from the plurality ofblocks to produce a corresponding color value for each of the blocks.Each corresponding color value indicates an average color of pixelswithin a respective block.

In step 630, image manager 140 sorts the plurality of blocks accordingto the variation values produced for the blocks. Image manager 140 canfurther, optionally, sort the plurality of blocks according to the colorvalues produced or identified for the blocks.

In step 640, image manager 140 organizes the plurality of blocks into asecond plurality of graphical digital image files, each respectivegraphical digital image file from the second plurality of graphicaldigital image files is created from blocks having similar variationvalues. For example, a given collection of images is comprised of 1000images with each image have dimensions of 1280×1024 pixels. If imagemanager 140 divides each image into blocks of 8×8 pixels, then eachimage would have 20,480 blocks. Summing blocks from the entire imagecollection, image manager 140 would then extract 20,480,000 blocks.Image manager 140 would then use these 20,480,000 blocks to create asecond plurality of images. The second plurality of images would have asum total of 20,480,000 blocks, but not necessarily the same number ofimages (1000) or same size images. Image manager 140 might group allblocks of a particular blue color and variation into an image withdimensions of 5000×5000 pixels (larger than images from the firstplurality of images). Image manager 140 might create another imagecomprised of blocks having high variation values with a created imagehaving dimensions of, for example, 400×600 pixels. A total number ofcreated images in the second plurality might be 1000 images, 222 images,4040 images, and so forth. Thus, the second plurality of graphicaldigital image files can have a quantity of graphical digital image filesdifferent than a quantity of graphical digital image files from thefirst plurality of graphical digital image files.

Image manager 140 can also create each respective graphical digitalimage file from the second plurality of graphical digital image filesusing blocks having similar color values. In some cases, a block withvarious shades of green can have a similar or identical variation valueas another block having various shades of yellow. To avoid creatingblocks with mixed colors, image manager 140 can further create newimages based on color in addition to variation values.

Image manager 140 can optionally identify a level of image compressionto apply to each respective graphical digital image file, from thesecond plurality of graphical digital image files, based on variationvalues produced for blocks from each respective graphical digital imagefile. The level of image compression affects image quality, meaning thatthe compression can visually degrade an image. The level of imagecompression indicates a compression setting of a predetermined imagecompression algorithm. This compression setting corresponds to aspecific compression ratio. For example, a predetermined imagecompression algorithm might provide ten different compression levelsranging from no image degradation to severe image degradation (but witha small file size).

For graphical digital image files comprised of blocks having smallvariation values in comparison to other blocks in the plurality ofblocks, image manager 140 can identify a low compression setting toapply to each corresponding respective graphical digital image file toresult in a compressed image file having relatively high image qualityin comparison to other graphical digital image files from the secondplurality of graphical digital image files.

For graphical digital image files comprised of blocks having largevariation values in comparison to other blocks in the plurality ofblocks, image manager 140 can identify a high compression setting toapply to each corresponding respective graphical digital image file toresult in a compressed image file having relatively low image quality incomparison to other graphical digital image files from the secondplurality of graphical digital image files.

In step 650, image manager 140 compresses each respective graphicaldigital image file, from the second plurality of graphical digital imagefiles, using a predetermined image compression algorithm. After blocksare extracted, analyzed and reorganized into a collection of new images,image manager 140 then applies an image compression algorithm to eachindividual newly created image. Each respective new image has blockswith similar values, thus image compression will be inherently moreefficient.

Image manager 140 can then apply the predetermined image compressionalgorithm to each respective graphical digital image file from thesecond plurality of graphical digital image files using the level ofimage compression identified to apply to each respective graphicaldigital image file.

Image manager 140 can also execute several additional steps. Imagemanager 140 can maintain a storage map that identifies a location ofeach block in the first plurality of graphical digital image files andthat identifies a location of each block in the second plurality ofgraphical digital image files. The storage map assists withreconstructing images. Image manager 140 decompresses each respectivegraphical digital image file, from the second plurality of graphicaldigital image files, using the predetermined image compressionalgorithm. Subsequently, image manager 140 extracts the plurality ofblocks from decompressed graphical digital image files from the secondplurality of graphical digital image files, and uses the storage map toreconstruct graphical digital image files from the first plurality ofgraphical digital image files using decompressed blocks.

With reorganization of blocks into a collection of new images, imagemanager 140 can produce an optimized set of images for the JPEG format.The JPEG algorithm does not apply a different compression setting todifferent blocks within a single image. Thus image manager 140 providesthe benefit of using different compression settings for different areasof a given image because the collection of images is reorganized into anew plurality of images enabling the JPEG specification to providevaried compression on an image-by-image basis or based on groupings ofimages.

In other embodiments, the compression setting can be dynamic. Forexample a user can decide that portions of images depicting an ocean arenot important and can receive more image degradation, while other areasof that image, such as human faces, should maintain high image quality.Image manager 140 can then identify and track such areas forcorresponding compression. For example, even though a facial regionmight have relatively higher variation values, a particular user may notwant any degradation on these regions. Image manager 140 can then createan image(s) comprised of blocks within a facial region to apply a lowerlevel of image compression. Image manager 140 enables a user todynamically select areas of the image to apply specified levels ofcompression.

In one embodiment, image manager 140 can be used for creating a PDFportfolio. A PDF portfolio is a file that embeds attachments withinwhich a user can navigate. Thus the PDF portfolio is a PDF filecontaining a navigator and embedded files and can function as a type offile archive.

In another embodiment, image manager 140 can compress a collection ofimages to yield a predetermined file size. For example, a user desiresto share the entire contents of a digital camera, which could be severalhundred megabytes. The user, however, desires to limit the total size ofthe collection of images to be able to fit on a CD or DVD. Image manager140 can then compress images from the digital camera to all fit on agiven CD or DVD media. The result is storing the collection of images onthe media, but with an image quality about two to four times greaterthan without using image manager 140.

Those skilled in the art will understand that there can be manyvariations made to the operations of the user interface explained abovewhile still achieving the same objectives of the invention. Suchvariations are intended to be covered by the scope of this invention. Assuch, the foregoing description of embodiments of the invention are notintended to be limiting. Rather, any limitations to embodiments of theinvention are presented in the following claims.

The invention claimed is:
 1. A computer-implemented method to performpreprocessing, compression, and archiving of a collection of digitalimage files comprising pixels, the method comprising: with a processor,receiving a plurality of digital image files from a collection ofdigital image files; in response to receiving the digital image files,extracting blocks, with the processor, from each of the digital imagefiles, wherein each block comprises a subset of the pixels of therespective image file; analyzing, with the processor, each of the blocksto determine a corresponding frequency of pixel data variation for eachof the blocks; creating, with the processor, a plurality of new digitalimage files each comprising a plurality of the blocks extracted from atleast two of the received digital image files, wherein the blocksselected for each particular new digital image file are those of theblocks of a given range of frequencies of pixel data variation;identifying, with the processor, a level of image compression to applyto each particular new digital image file such that the level of imagecompression is optimized for the frequencies of pixel data variation ofthe blocks in the particular new digital image file; compressing, withthe processor, each particular new digital image file using the level ofimage compression identified to apply to the particular new digitalimage file; and with the processor, creating an image archive comprisingthe plurality of compressed new digital image files and storing theimage archive to a memory.
 2. The computer-implemented method of claim1, further comprising: analyzing, with the processor, each of the blocksto produce a corresponding color value for each of the blocks, eachcorresponding color value indicating an average color of pixels within arespective block, wherein the blocks selected for each particular newdigital image file are those of the blocks of a predetermined colorrange.
 3. The computer-implemented method of claim 1, wherein theidentified level of image compression to apply to each particular newdigital image file indicates a compression setting of a predeterminedimage compression algorithm; and wherein compressing each particular newdigital image file includes applying the predetermined image compressionalgorithm to the particular new digital image file according to theapplicable identified level of image compression.
 4. Thecomputer-implemented method of claim 3, wherein each particular newdigital image file is compressed using a Joint Photographic ExpertsGroup (JPEG) compression algorithm.
 5. The computer-implemented methodof claim 1, wherein identifying a level of image compression to apply toeach particular new image file includes: for each particular new digitalimage file having small pixel data variation values in comparison toothers of the new digital image files, identifying, with the processor,a low level of image compression to apply to the particular new digitalimage file, the low level of image compression resulting in a compressednew digital image file having relatively high image quality incomparison to others of the new digital image files; and for eachparticular new digital image file having large pixel data variationvalues in comparison to others of the new digital image files,identifying, with the processor, a high level of image compression toapply to the particular new digital image file, the high level of imagecompression resulting in a compressed new digital image file havingrelatively low image quality in comparison to others of the new digitalimage files.
 6. The computer-implemented method of claim 1, furthercomprising: with the processor, creating a storage map that identifies alocation of each block in the received plurality of digital image filesand storing the storage map in the memory; with the processor, accessingthe image archive from the memory and extracting the new digital imagefiles from the image archive; decompressing, with the processor, each ofthe new digital image files; and with the processor, accessing thestorage map from the memory and using the storage map to reconstruct theplurality of digital image files from the blocks of the decompressed newdigital image files.
 7. The computer-implemented method of claim 1,wherein the pixel data comprises one or more of color data, luminancedata, chrominance data, and hue data.
 8. A computer-implemented methodto perform preprocessing, compression, and archiving of a collection ofdigital image files comprising pixels, the method comprising: with aprocessor, receiving a plurality of digital image files from acollection of digital image files; extracting, with the processor,blocks from each of the digital image files wherein each block comprisesa contiguous subset of the pixels of the respective image file;analyzing, with the processor, each of the blocks to determine acorresponding frequency of pixel data variation for each of the blocks;creating, with the processor, a plurality of new digital image fileseach comprising a plurality of the blocks extracted from at least two ofthe received digital image files, wherein the blocks selected for eachparticular new digital image file are those of the blocks of a givenrange of frequencies of pixel data variation; generating, with theprocessor, a storage map that identifies a location of each block in theplurality of digital image files from a collection of digital imagefiles and that identifies a corresponding location of each block in thenew digital image files; compressing, with the processor, eachrespective new digital image file using a predetermined imagecompression algorithm; and with a processor, creating an image archivecomprising the plurality of compressed new digital image files and thegenerated storage map, and storing the image archive to a memory.
 9. Thecomputer-implemented method of claim 8, further comprising: identifying,with the processor, a level of image compression to apply to eachparticular new digital image file based on the frequencies of pixel datavariation of the selected blocks comprised by each respective newdigital image file, wherein the identified level of image compression toapply to each particular new digital image file indicates a compressionsetting of the predetermined image compression algorithm, thecompression setting corresponding to a compression ratio; and applying,with the processor, the predetermined image compression algorithm toeach particular new digital image file using the correspondingidentified level of image compression.
 10. The computer-implementedmethod of claim 9, wherein identifying the level of image compressioncomprises: for the new digital image files comprised of blocks havinglow frequencies of pixel data variation in comparison to other newdigital image files, identifying, with the processor, a low compressionsetting to apply to each corresponding new digital image file togenerate a compressed digital image file having relatively high imagequality in comparison to other digital image files generated from newdigital image files comprised of blocks having relatively highfrequencies of pixel data variation; and for the new digital image filescomprised of blocks having high frequencies of pixel data variation incomparison to other new digital image files, identifying, with theprocessor, a high compression setting to apply to each corresponding newdigital image file to generate a compressed digital image file havingrelatively low image quality in comparison to other digital image filesgenerated from new digital image files comprised of blocks havingrelatively low frequencies of pixel data variation.
 11. Thecomputer-implemented method of claim 8, further comprising: analyzing,with the processor, each of the blocks to produce a corresponding colorvalue for each of the blocks, each corresponding color value indicatingan average color of pixels within a respective block, wherein the blocksselected for each particular new digital image file are those of theblocks of a predetermined color range.
 12. The computer-implementedmethod of claim 8, wherein the quantity of received digital image filesis different from the quantity of new digital image files.
 13. Thecomputer-implemented method of claim 8, wherein the pixel data comprisesone or more of color data, luminance data, chrominance data, and huedata.
 14. A computer program product including a non-transitorycomputer-storage medium having instructions stored thereon forpreprocessing, compression, and archiving of a collection of digitalimage files comprising pixels, such that the instructions, when carriedout by a processing device, cause the processing device to perform theoperations of: receiving a plurality of digital image files from acollection of digital image files; extracting blocks from each of thedigital image files wherein each block comprises a subset of the pixelsof the respective image file; analyzing each of the blocks to determinea corresponding frequency of pixel data variation for each of theblocks; creating a plurality of new digital image files each comprisinga plurality of the blocks extracted from at least two of the receiveddigital image files, wherein the blocks selected for each particular newdigital image file are those of the blocks of a given range offrequencies of pixel data variation; identifying a level of imagecompression to apply to each particular new digital image file such thatthe level of image compression is optimized for the frequencies of pixeldata variation of the blocks in the particular new digital image file;compressing each particular new digital image file using the level ofimage compression identified to apply to the particular new image; andcreating an image archive comprising the plurality of compressed newdigital image files and storing the image archive to a memory.
 15. Thecomputer program product of claim 14, further comprising instructionsfor causing the processing device to perform the operations of:analyzing each of the blocks to produce a corresponding color value foreach of the blocks, each corresponding color value indicating an averagecolor of pixels within a respective block, wherein the blocks selectedfor each particular new digital image file are those of the blocks of apredetermined color range.
 16. The computer program product of claim 14,further comprising instructions for causing the processing device toperform the operations of: identifying the level of image compression toapply to each respective new digital image file, wherein the identifiedlevel of image compression to apply to each particular new digital imagefile indicates a compression setting of a predetermined imagecompression algorithm; and wherein compressing each particular newdigital image file includes applying the predetermined image compressionalgorithm to the particular new digital image file according to theapplicable identified level of image compression.
 17. The computerprogram product of claim 16, wherein the processing device performs theoperations of: compressing each particular new digital image file usinga Joint Photographic Experts Group (JPEG) compression algorithm.
 18. Thecomputer program product of claim 14, wherein identifying a level ofimage compression to apply to each particular new image file includes:for each particular new digital image file having small pixel datavariation values in comparison to others of the new digital image files,identifying a low level of image compression to apply to the particularnew digital image file, the low level of image compression resulting ina compressed new digital image file having relatively high image qualityin comparison to others of the new digital image files; and for eachparticular new digital image file having large pixel data variationvalues in comparison to others of the new digital image files,identifying a high level of image compression to apply to the particularnew digital image file, the high level of image compression resulting ina compressed new digital image file having relatively low image qualityin comparison to others of the new digital image files.
 19. The computerprogram product of claim 14, further comprising instructions for causingthe processing device to perform the operations of: creating a storagemap that identifies a location of each block in the received pluralityof digital image files and storing the storage map in the memory;decompressing each of the new digital image files; and accessing thestorage map from the memory and using the storage map to reconstruct theplurality of digital image files from the blocks of the decompressed newdigital image files.
 20. The computer program product of claim 14,wherein the pixel data comprises one or more of color data, luminancedata, chrominance data, and hue data.
 21. A computer system configuredfor preprocessing, compression, and archiving of a collection of digitalimage files comprising pixels, the system comprising: a processor; amemory coupled to the processor, the memory storing instructions thatwhen executed by the processor cause the system to perform theoperations of: receiving a plurality of digital image files from acollection of digital image files; extracting blocks from each of thedigital image files, wherein each block comprises a subset of the pixelsof the respective image file; analyzing each of the blocks to determinea corresponding frequency of pixel data variation for each of theblocks; creating a plurality of new digital image files each comprisinga plurality of the blocks extracted from at least two of the receiveddigital image files, wherein the blocks selected for each particular newdigital image file are those of the blocks of a given range offrequencies of pixel data variation; identifying a level of imagecompression to apply to each particular new digital image file such thatthe level of image compression is optimized for the frequencies of pixeldata variation of the blocks in the particular new digital image file;compressing each particular new digital image file using the level ofimage compression identified to apply to the particular new image; andcreating an image archive comprising the plurality of compressed newdigital image files and storing the image archive to a memory.
 22. Thecomputer system of claim 21, wherein the pixel data comprises one ormore of color data, luminance data, chrominance data, and hue data. 23.A computer program product including a non-transitory computer-storagemedium having instructions stored thereon for decompression andreconstruction of original digital image files comprising pixels from animage archive, such that the instructions, when carried out by aprocessing device, cause the processing device to perform the operationsof: receiving an image archive comprising a plurality of compresseddigital image files, wherein each compressed digital image file iscomprised of blocks of pixels having a given range of frequencies ofpixel data variation; receiving a storage map that identifies a currentlocation of each block in one of the compressed digital image files andthat identifies an original location of each block in one of a pluralityof original digital image files; decompressing each respective digitalimage file using a level of decompression corresponding to a frequencyof pixel data variation of its blocks of pixels; extracting a pluralityof decompressed blocks from the decompressed digital image files; andusing the storage map to reconstruct the plurality of original digitalimage files from the decompressed blocks, wherein each original digitalimage file comprises at least one decompressed block from more than oneof the plurality of compressed digital image files.
 24. A computerprogram product including a non-transitory computer-storage mediumhaving instructions stored thereon for preprocessing, compression, andarchiving of a collection of digital image files comprising pixels, suchthat the instructions, when carried out by a processing device, causethe processing device to perform the operations of: receiving aplurality of digital image files from a collection of digital imagefiles; extracting blocks from each of the digital image files, whereineach extracted block comprises a contiguous subset of the pixels of therespective image file; analyzing each of the blocks to determine acorresponding frequency of pixel data variation for each of the blocks;creating a plurality of new digital image files each comprising aplurality of the blocks extracted from at least two of the receiveddigital image files, wherein the blocks selected for each particular newdigital image file are those of the blocks of a given range offrequencies of pixel variation; generating a storage map that identifiesa location of each block in the plurality of digital image files fromthe collection of digital image files and that identifies acorresponding location of each block in the new digital image files;compressing each respective new digital image file using a predeterminedimage compression algorithm; and creating an image archive comprisingthe plurality of compressed new digital image files and the generatedstorage map, and storing the image archive to a memory.
 25. The computerprogram product of claim 24, wherein the pixel data comprises one ormore of color data, luminance data, chrominance data, and hue data.